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Latin American and Caribbean Ethnic Studies

ISSN: 1744-2222 (Print) 1744-2230 (Online) Journal homepage: http://www.tandfonline.com/loi/rlac20

The impact of race and social economic status onuniversity admission at the University of São Paulo

Rubia R. Valente

To cite this article: Rubia R. Valente (2016): The impact of race and social economic statuson university admission at the University of São Paulo, Latin American and Caribbean EthnicStudies, DOI: 10.1080/17442222.2016.1170955

To link to this article: http://dx.doi.org/10.1080/17442222.2016.1170955

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The impact of race and social economic status on universityadmission at the University of São PauloRubia R. Valente

School of Economic, Political and Policy Sciences, University of Texas, Richardson, TX, USA

ABSTRACTThis paper explores the relationship between race, class, andaccess to higher education by analyzing the characteristics ofstudents admitted to the University of São Paulo (USP) in Brazilfrom 2004 to 2008. Employing novel data, the logistic regressionresults indicate that those accepted at USP are more likely to bewhite, from affluent families, to have studied in private highschools, to have enrolled in prep courses, and to have a motherwho attained higher education. The findings posit that the lack ofaccessibility for nonwhites and lower income status students tohigher education in Brazil, as exemplified by this case study, is animpediment to social mobility. The implications of these findingsfor future research and policy are discussed.

KEYWORDSAffirmative action; socialclass; race; universityadmissions; University of SãoPaulo; FUVEST; vestibularadmission

Introduction

An incongruity of the Brazilian educational system is the fact that while well-regardedinstitutions at the primary and secondary levels are in the private sector, at the uni-versity level, it is public institutions that are seen as superior. Consequently, admission topublic universities is a rigorous, competitive process. Poor students who cannot afford aprivate high school education perform worse in the vestibular1 than students fromprivate schools. Furthermore, since Afro-descendants have been disproportionatelysituated at the bottom of the socioeconomic ladder, the majority of nonwhite studentsare enrolled in public schools. This creates an intergenerational cycle of socioeconomicstagnancy, where social mobility is practically unattainable for poor and nonwhite2

students with little access to higher education. This theoretical framework, advancedby Hasenbalg (1979), posits cumulative racial disadvantage over the life cycle, whichinfluences socioeconomic position in the labor market, and thus an individual’s statusduring adulthood.

Affirmative action policies have been implemented in Brazilian universities since 2004with the objective of reducing inequality of access (Johnson and Heringer 2015; Telles2004). In 2012, the Brazilian government passed into law the Lei das Cotas, or AffirmativeAction Law, which prescribes admission quotas based on social and racial indicators forall federal universities. This law was surrounded by an intense public debate on whetheraffirmative action is appropriate as a mean to promote equitable access to higher

CONTACT Rubia R. Valente rubiavalente@utdallas.edu

LATIN AMERICAN AND CARIBBEAN ETHNIC STUDIES, 2016http://dx.doi.org/10.1080/17442222.2016.1170955

© 2016 Informa UK Limited, trading as Taylor & Francis Group

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education in the Brazilian case.3 Many assumptions have been made for and againstaffirmative action policies, yet empirical research indicating the actual relationshipsbetween class, race, and access to higher education in Brazil is not voluminous.

Most studies on higher education accessibility have focused on the effects of affir-mative action policies after its implementation and whether these policies have effec-tively expanded access of nonwhite students. Francis and Tannuri-Pianto (2012a), forexample, analyzed the effect of racial quota at the University of Brasilia (UnB) and foundthat affirmative action policies significantly raised the proportion of black and dark-skinned students. Golgher, de Lima Amaral, and Coelho Neves (2014) analyzed theperformance of quota students after admission to the Federal University of MinasGerais (UFMG) for the year of 2009 and 2010, and found that affirmative action hadthe desired effect of including nonwhites and poor students without diminishingeducational quality. Valente and Berry (forthcoming) analyzed the performance ofquota students at the national level using the Exame Nacional de Desempenho dosEstudantes (ENADE) 4 exam from 2009 to 2012, and found that affirmative actionstudents perform at similar levels of students admitted through traditional methods inpublic universities. These studies conjecture that affirmative action policies have been aneffective mechanism for expanding access of nonwhites and poor youth to universitiesin Brazil – without compromising the quality of education. However, while valuableinsight is provided into the effectiveness of affirmative action policies, these studies failto analyze the individual effects of race and socioeconomic status on the traditionaladmission process. Such analysis is critical to justify the application and measure theimpact of racial and social quotas in the context of the Brazilian higher educationsystem.

Few studies have focused on the empirical relationship between class, race, andaccess to higher education in Brazil. Francis and Tannuri-Pianto (2012b), found evidencethat race, socioeconomic status, and gender were considerable barriers to collegeadmission to college attendance and achievement at the UnB. Likewise, Guimarãesand Sampaio (2013), when examining the Federal University of Pernambuco (UFPE),found that native Brazilians and blacks performed worse than white students in thevestibular exam of 2005. They also found that students from higher income families whoattended private school performed much better in the exam. Similarly, Griner, BezerraSampaio, and Bezerra Sampaio (2015), when examining the Federal University of RioGrande do North (UFRN) vestibular of 2010, found that nonwhite and poor studentsperformed worse, reducing their chances of admission. More recently, Valente (forth-forthcoming) used the Exame Nacional do Ensino Médio (National Secondary EducationExam, or ENEM) to analyze the relationship between race and access to higher educationnationwide between 2004 and 2008. The results indicate that race and socioeconomicstatus are significant predictors of university acceptance in Brazil – nonwhites andstudents from lower socioeconomic background perform worse on the ENEM and aretherefore less likely to be admitted into college.

This paper contributes to the literature by employing novel data to quantify theinequitable distribution of access to higher education in the University of São Paulo(USP), the largest and most prestigious Brazilian university. Our analysis provides theactual metrics (odds ratio) of racial discrimination in the university admission process,and captures for the first time the likelihood of being admitted to USP. This study uses a

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high-quality dataset in combination with data provided by Fundação Universitária parao Vestibular (FUVEST, or the University Foundation for Vestibular)5 to analyze whethersocioeconomic status and race influence students’ chances of being admitted to USP.This case study shows that Hasebalg’s theory of cumulative racial disadvantage is validand applicable in understanding how race and class inequalities impact the advance-ment of nonwhites in Brazil by impeding their ability to pursue higher education.

This study also advances our comprehension of the economics of educational attain-ment, particularly as it relates to race and socioeconomic status in Brazil – most studiesdo not provide empirical evidence for racial disparities in university admission, employ-ing only descriptive statistics or theoretical frameworks (see Guimarães 2007; Hasenbalgand Valle Silva 2013; Osório 2009; Queiroz 2003). The probability distributions predictedby the models presented in this study indicate a significant racial disparity for accep-tance at USP, providing support for an argument in favor of racial affirmative action andcorroborating Hasebalg’s theory of racial disadvantage among nonwhites in Brazil.

This paper is organized as follows. Section 2 briefly explores the inequality in educa-tional opportunity in Brazil and the vestibular exam. Section 3 focuses on the proposedcase study, discussing the dataset, key hypotheses, model, and variables description.Section 4 presents the logistics and analyzes the results for each year, and Section 5provides a discussion of these results. Section 6 addresses the limitations of our study,and Section 7 draws together the main conclusions and presents implications for futureresearch.

Race and education in Brazil

Educational opportunity in Brazil

Institutions created during Brazil’s Iberian colonization gave rise to a style of governancein which only few European descendants had power and wealth while Afro-descendants,despite being the majority, were exploited by the elite through slavery (Stohl and Lopez1984).6 Despite rhetoric of inclusion (Freyre 1933; Pierson 1945), racial discrimination haspersisted and race has been consistently used to exclude nonwhites throughout Brazil’shistory from social, economic, and political positions (Bastide and Fernandes 1959;Cardoso and Ianni 1960; Costa Pinto 1952; Nascimento 1989; Nogueira 1985; Telles1992, 2004). This phenomenon is observed in the educational system, which is notequally accessible to all racial groups; the per centage of whites, blacks, and browns inprivate high schools is respectively 56.58 per cent, 5.63 per cent, and 32 per cent.Conversely, in public high school, the observed distribution is 34 per cent for whites,13.6 per cent for blacks, and 45 per cent for browns (Guimarães and Sampaio 2007).Similarly, when analyzing institutions of higher education, one finds that, althoughnonwhites make up 50.7 per cent of the Brazilian population, they represent fewerthan 35.8 per cent of the students attending Brazilian universities (IBGE 2010).

This disparity is accentuated when looking at individual universities. For example,even though nonwhites represent 44.3 per cent of the population in Rio the Janeiro,they correspond to a mere 20 per cent of students enrolled at the Federal University ofRio de Janeiro. (IBGE 2000; Telles 2004) Comparably, in the state of Bahia, where 75 percent of the population is nonwhite, there are only 42.6 per cent nonwhite students at

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the Federal University of Bahia (IBGE 2000; Vogt 2003). A greater discrepancy can beobserved in the state of São Paulo, where nonwhites represent 34.3 per cent of thepopulation, yet only 8.3 per cent of students at USP are nonwhites (USP 2002).

Nonwhite students have historically had educational disadvantages partly due totheir inability to pay for costly private education and to the dreadful conditions of thefree education offered in public primary and secondary schools.7 The overall academicperformance of students in public schools is much inferior compared to students inprivate schools due to many flaws, including lack of infrastructure, lack of good andqualified teachers, lack of funding, and a feeble curriculum (Akkari 2001; Guimarães andSampaio 2007). Since private education is extremely expensive in Brazil, the simple factof pursuing a private school education is a direct indication of one’s higher economicstatus, which is also positively related to the level of their parents’ education. The familyincome of a student at a private school is on average three times the family income of astudent in a public school, and parents of children in a private school have on average 4or more years of education than the parents of children in a public school (Guimarãesand Sampaio 2007; de Castro 1997).

These factors result in a favorable environment for the intellectual development ofhigher status students, who have much greater chances of being admitted to free, publicuniversities (de Castro 1997). On the other hand, the inadequate provision of publicprimary and secondary education hampers the ability of poor and nonwhite populationsto compete for places in public universities, and therefore gain the benefits of auniversity education (Beltrão and Novellino 2002; Cunha 1986; Cury 1989; Guimarãesand Sampaio 2007, 2009; Saviani 2011; Telles 2004). In addition, the lack of financialresources to afford private college education and the fierce competition of the vestibularexam in public universities make access to higher education unreachable to mostnonwhite students (Carvalho 2005; Feres and Zoninsein 2006, 2008; Guimarães 2003;Telles 2004; Paixão 2011).

Race and higher education in Brazil: the vestibular

University access in Brazil is a competitive process. Public universities are not only free,but are also the most prestigious universities in the country. It is estimated by theMinistry of Education that 5,181,699 students struggled to get access to one of the2,629,598 available university slots in 2006 (INEP 2008). For some majors, such asMedicine, Engineering, and Law, the ratio of students to available places can be ashigh as 20 or more in public universities (Carvalho and Magnac 2010). Different fromother countries, such as the United States, which uses multiple criteria for admission,Brazil until very recently only used an objective-grading exam called the vestibular.8

The vestibular has the following general features. Students must choose a singleundergraduate major before taking the test and will compete against students whomade the same choice (Carvalho and Magnac 2010). The exam comprises of manysubexams, each one evaluating knowledge in a specific subject, for example,Mathematics, Physics, Chemistry, Biology, Portuguese, History, Geography, and aForeign Language (Carvalho and Magnac 2010). In general, the vestibular consists oftwo stages. The first stage is common to all majors and the second stage is more specificto each major. The different departments within the university that provides the

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undergraduate major courses can weigh the subexams differently in order to reflecttheir priorities. All candidates must pay a fee before taking the vestibular, which variesdepending on the university. Some universities waive this fee for students based onfinancial needs. Once the students take the exam, the various departments rank all theirapplicants by grade and reject those exceeding the number of seats for each major(Carvalho and Magnac 2010). Generally, students can have up to three choices of major.If they are not accepted into their primary choice, they can be considered for their othersecondary choices, if the vestibular grade makes the cut for those majors.

The vestibular is not equally fair to all students. Cavalcanti, Guimarães, and Sampaio(2010) quantified the difference in performance of public and private school students inthe vestibular of a major public university in Pernambuco, Brazil. They established thatthe Brazilian higher education system is an important channel for the persistence ofinequality, and that going to a private high school versus a public high school matterstremendously when taking the vestibular. This creates a cruel paradox because given thehighly competitive nature of the vestibular, it is hard to enter a public university andreceive a free college education without having previously received a private highschool education (McCowan 2007). Thus, access to high-quality, free higher educationis limited to the higher socioeconomic groups, resulting in great racial inequity, with lowrepresentation of African Brazilians and indigenous peoples (INEP 2003; McCowan 2007;Paixão 2011). A study by Tomelin (2002) analyzed the opportunities for higher educationin Brazil from the colonial period to 2000 and found that there is a long-lasting limitationon access to higher education for segments of the Brazilian society on the basis of raceand social class. Other researchers have argued that education is one of the foundationsof the economic and social development of a society because it results in the growth ofthe individual’s economic and social status in the society, and the economic growth ofthe country as a whole (de Castro 1997; Castro and Guimarães 1993; Guimarães andSampaio 2009; Paixão 2011; Telles 2004). This is the reason why it is important tounderstand whether the admission process is a fair tool for admission used by univer-sities, or if it is an impediment to social mobility in Brazil. Our work builds upon thesestudies by testing the relationship of both race and socioeconomic status to theadmission process. It seeks to provide insights to the following questions: are nonwhitestudents not admitted to college because of their socioeconomic status and lack ofaccess to private high school, or does race also play a role in the admission process?How much of these inequalities are associated with race and not only socioeconomicstatus? Are affirmative actions based on race and socioeconomic status justifiable?

According to Hasenbalg’s (1979) theory of cumulative racial disadvantage over thelife cycle, race is an additional factor to class when analyzing inequality in Brazil andaccess to education plays a central and vital role in this process. Nonwhite children havehigher odds of being born poor and are more likely to suffer poverty than whitechildren. In addition, their odds of attending school are smaller than that of whitechildren, and when they do, they attend public schools, which are not as good as theprivate ones. As a result, they are less likely to pass the vestibular and attend university(Paixão 2011; Telles 2004). Accordingly, their low educational achievement will lead tolower pay and depleted jobs, mainly in the informal sector. When a new cohort ofnonwhites gets to the labor market, the differences between their educational standardsand that of whites in the same cohort is so severe that is impossible for them to

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compete and move up the socioeconomic ladder. This cycle perpetuates for futuregenerations as educational opportunity is stratified by race and social class.

Given this theoretical framework, two hypotheses are proposed:

(1) Student’s admission to university, or ‘passing the vestibular,’ will be predicted bythe student’s socioeconomic status and by the student’s race.

(2) Students from low-income households, and nonwhite students, are less likely tobe admitted to university in comparison with high income, white students.

Exame Nacional do Ensino Médio

The Exame National do Ensino Médio (ENEM) is a nationwide exam given yearly tograduating seniors and high school graduate students in Brazil to assess the quality ofprecollege education. The exam was created in 1998 by the Instituto Nacional deEstudos e Pesquisas Educacionais (National Institute of Studies and EducationalResearch, or INEP). It is divided into two parts: an objective part with 63 multiple choicequestions, and an essay part. Since 1999, many universities have used the ENEM as acomplement to the vestibular.9 In 2009, the Ministry of Education proposed the Sistemade Seleção Unificada (SISU), which is a system that selects candidates to FederalUniversities and Federal Institutes based on their ENEM score, thus abolishing thevestibular.10 Since then, 101 institutions of higher education have adopted the ENEMexam instead of the vestibular for their admission processes11 (Brazilian Ministry ofEducation 2011). Currently, about 8 million students take the ENEM exam every year.

Method

Data

In order to test these hypotheses, the admission process at USP was analyzed. USP’svestibular is prepared and administered by a federal agency called FUVEST, which alsogives a socioeconomic questionnaire to all applicants of the vestibular.12 The only datapublicly available are the summary statistics for each major. After contacting FUVEST, weobtained the mean ENEM score and standard deviation for the objective part of theENEM exam for each admitted class at USP during the years of 2004–2008 by major.13

Since the ENEM socioeconomic questionnaire14 is available online and contains thecharacteristics for all students who took the ENEM exam during 2004–2008, dummyvariables (based on the average objective ENEM score of admitted students) werecreated to indicate whether the respondents would have the same ENEM score ofadmitted USP students on average. The maximum score on the objective part of theENEM exam was 63 for all years analyzed.

INEP provides online microdata using each student taking the ENEM exam as unit ofanalysis. The dataset of students who took the ENEM does not indicate whether astudent actually applied to the vestibular at USP. Thus, the data were filtered to includeonly students in the state of São Paulo in order to increase the likelihood that theobserved data would overlap students who actually took the USP vestibular. An

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important law of geography states that, ‘everything is related to everything else, butnear things are more related than distant things’ (Tobler 1970). Although it might reducestatistical significance due to a decrease in the number of students, it is well known inthe literature that the quality of education, both in public and private high schools inSão Paulo, is superior to that in all other states, as is the case for other variables, such asstandard of living and income. In addition, the majority of students who apply to USPare from the state of São Paulo. Hence, it is fitting to consider just the students from thisstate in this analysis.

Instead of looking at majors exhaustively15 for each year, we analyzed the major withthe highest average ENEM score and the major with the lowest average ENEM score.Thus, two dummy variables were created in the main dataset to indicate threshold foracceptance at USP in these two categories. The data were filtered to include onlystudents who answered all questions relevant to the study. Each year was analyzedindividually and logistic regression models were used. Table 1 provides a summary and adescription of variables used in the analysis. Table 2 presents a small subset of majors atUSP, including the most and least competitive ones, with their respective average ENEMscores and standard deviations. Throughout the analyzed period, the highest ENEMscores were observed in the Medicine major, and the lowest, in the Natural Sciences16

(2006–2008) and Fonoaudiology17 (2004–2005) majors.

Explanatory variables

RaceDummy variables for race (i.e., black, brown, indigenous, and Asian) were created toanalyze the effects of each particular race, using white as the reference category. In orderto better describe these racial categories, it is important to provide a brief context of

Table 1. Student-level variables.Variable Definition and source

RaceWhite 1 (yes); 0 (no) – (reference category)Black 1 (yes); 0 (no)Brown 1 (yes); 0 (no)Asian 1 (yes); 0 (no)Indigenous 1 (yes); 0 (no)

Female 1 (female); 0 (male)Age Student’s ageFamily income All wages by monthly minimum wage: 0 (low) to 7 (high)Public school Type of high school attended: 1 (public); 0 (private)Prep course Attended cursinho for the vestibular? 1 (yes); 0 (no)Mother educationLess than high schoolHigh school graduate

1 (yes); 0 (otherwise) – (reference category)1 (yes); 0 (otherwise)

Some college 1 (yes); 0 (otherwise)Bachelor degree 1 (yes); 0 (otherwise)Unknown 1 (yes); 0 (otherwise)

Escore ENEM score: min 0, max 63ENEM score thresholdsPmed Meets threshold for acceptance in Medical School at USPPnsciPfono

Meets threshold for acceptance in Natural Sciences at USPMeets threshold for acceptance in Fonoaudiology at USP

Source: Instituto Nacional de Estudos e Pequisas and FUVEST.

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race classification in Brazil. One of the most unique distinctions of Brazilian race relationsin comparison with other interracial systems is the plethora of racial terms,18 and theabstract and referential ambiguity surrounding their usage (Harris 1964). In the UnitedStates, for example, race identity is defined by descent, while in Brazil it is a muchcomplicated concept that involves mostly physical appearance (hair texture, shape oflips, and nose) and skin color or pigmentation, but can also be subjected by class criteria(education and wealth). Since race is not purely defined on biological grounds, it is opento interpretation (Skidmore and Smith 1997). This concept of Brazilian race allows formultiple interpretations of certain characteristics that complicate the distinguishing ofthose on the border of whiteness and brownness (Reiter and Mitchell 2010). Forexample, to be black in Brazil one has to be at the dark end of the color spectrum,while partially black in ethnic origin means being black in the United States. Thus, manymulatos (mixed of black and white) escape negative stigmatization through ‘passing’ aswhite (Marcus 2012; Skidmore 1993; Telles 1992). As Telles wrote, ‘persons identifying aswhite or black often are not “racially pure” but are “relatively white or relatively black,”and there is a tendency for persons on the border of a color category to “pass” into thelighter category’ (Telles 1992, 187). Passing allows for further economic and socialmobility in light of the mulato’s physical approximation to ‘looking white.’ Many scholarshave argued that the social and economic position of black and mulatos in Braziliansociety is not the same (Daniel 2006; Marx 1998; Nogueira 2006). Holding an intermedi-ary position between whites and blacks, some argue that pardos face less prejudice andhave advantages over blacks both socially and economically in Brazil (Daniel 2006;Degler [1971] 1986; Nogueira 2006; Wade 1997). Nevertheless, a significant and growingnumber of social scientists, such as Carvalho (2005), Guimarães (1997, 1998, 1999, 2002,2003, 2002), Paixão (2003, 2006), Santos and Silva (2005), Silvério (2003, 2002) and manyothers, claim that both groups suffer from similar discrimination and prejudice in Brazil,and that the mulato does not have a privilege status over blacks, as previously argued byDegler ([1971] 1986) and others.

Therefore, in this study, the same racial categories employed by FUVEST are used,despite the fact that other studies have merged students who self-identify as blacks(negros) and browns (pardos or mulatos)19 under one category.20 Thus, both black,brown, as well as indigenous people and Asians (yellow) are included as different racecategories. Although indigenous and Asian groups are very small in Brazil, we cananticipate that indigenous students suffer the same or worse disadvantage as black

Table 2. ENEM Scores for students accepted at USP, 2008–2004.2008 2007 2006 2005 2004

Major* Mean S.D. Mean S.D. Mean S.D. Mean S.D. Mean S.D.

Medicine 60.05 1.95 56.63 2.26 58.51 2.07 59.65 1.92 59.01 2.12Engineering (POLI) 58.40 2.60 53.88 3.24 56.00 2.89 57.88 2.71 57.11 3.12Law 58.97 2.27 54.00 3.13 56.20 3.05 58.24 2.56 57.59 2.92Social Science 55.93 3.32 50.05 4.79 53.34 3.82 55.33 3.42 54.63 4.62Dentistry 54.33 4.39 48.08 4.50 51.02 3.81 53.54 3.33 52.28 4.21Physical Education 54.98 3.02 45.76 4.32 51.12 3.85 53.14 3.22 53.26 3.60Literature Studies 52.70 4.63 44.31 5.68 47.64 5.15 50.97 4.82 50.81 5.16Arts and Technology 51.10 3.57 42.83 5.24 46.37 4.86 50.46 5.00 na naNatural Sciences 45.83 7.14 33.87 7.04 40.45 5.61 38.31 8.05 na na

* There were a total of 107 majors in 2008, 105 majors in 2007, 101 majors in 2006, 98 in 2005, and 86 in 2004.

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and brown students. On the other hand, Asians-descendant students likely have anadvantage over the other racial groups, as they tend to be from socioeconomicallyprivileged families. It is important to note that due to the complex racial system in Brazil,a claim for blackness must be accepted, as there is no rule about who is black (Telles2004). Thus, in this study, we accept the students’ race claim.

Type of school and cursinho cursinho pré vestibularGiven the great racial disparity among students from public and private schools, wecontrol for type of school (de Castro 1997). In addition, wealthier students are able toafford preparatory courses that are specifically designed to prepare students to take thevestibular exam. These prep courses, curso pré-vestibular, or just cursinho, can be veryexpensive albeit some might be provided to disadvantaged students by volunteers forfree.21 In either case, having access to prep courses greatly increases the chances ofpassing the vestibular in a public university. Therefore, we also control for attendance inprep courses.

Other personal and family background characteristicsOther explanatory variables include gender, age, mother’s educational level, and familyincome (both measurements of socioeconomic status). Mother’s education containsdummy variables for each educational level, which has ‘less than high school’ as itsreference category. Whenever mother’s education was listed as unknown, a dummyvariable was created for the missing data.

In Brazil, household income is reported monthly and is set by monthly minimumwage: one monthly minimum wage was equivalent to R$260 in 2004, R$300 in 2005, R$350 in 2006, R$380 in 2007, and R$415 in 2008 (Guia Trabalhista 2014). Thus, instead ofusing a nominal value, the logit analyses use the categories set by the ENEM ques-tionnaire as following: no income; less than one minimum wage; between one and twominimum wages; between two and five minimum wages; between five and 10 minimumwages; between 10 and 30 minimum wages; between 30 and 50 minimum wages; andmore than 50 minimum wages. As a result, we can comparatively analyze income foreach year.

Results of logistic analyses

The logistic results are presented in Tables 3 and 4. The coefficients in these models areodds ratios, where a value greater than one indicates a positive relationship and a valueless than one points to a negative relationship. For each year, the odds ratios indicatewhich characteristics have the most influence for being accepted into USP at the highestand lowest ENEM threshold criteria. The odds ratios for a unit increase of each covariateof the response variable in all models (per cent change in odds) are included to ease theinterpretation of the logit results.

The results in Table 3 indicate that most interactions are significant for black andbrown students, revealing that nonwhite students are less likely to be accepted into thehighest ENEM threshold criteria (Medicine) than white students for each year analyzed.The odds ratio for a unit increase of the response variable in model H1 indicates that theodds of being accepted into Medicine are 36 per cent less for black students and 90 per

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Table3.

Logitregression

outputs(odd

sratio

andpercentchange

inod

ds)–high

estcriteria.

Variables

H1

H2

H3

H4

H5

Year

2008

Year

2007

Year

2006

Year

2005

Year

2004

pmed

%pm

ed%

pmed

%pm

ed%

pmed

%

Race Black

0.640*

−36.0

0.655***

−34.5

0.510*

−49.0

0.839

−16.1

0.652***

−34.8

Brow

n0.100*

−90.0

0.461***

−53.9

0.229***

−77.1

Indigeno

us1.250

25.0

0.973

−2.7

1.047

4.7

0.508

−49.2

Asian

1.841***

84.1

1.469***

46.9

2.092***

109.2

1.849***

84.9

1.603***

60.3

Female

0.369***

−63.1

0.428***

−57.2

0.251***

−74.9

0.320***

−68.0

0.311***

−68.9

Age

1.235***

23.5

0.883***

−11.7

0.995

−0.5

0.947

−5.3

0.962

−3.8

Publicscho

ol0.144***

−85.6

0.192***

−80.8

0.0408***

−95.9

0.057***

−94.3

0.133***

−86.7

Prep

course

1.791***

79.1

2.079***

107.9

1.418*

41.8

2.003***

100.3

2.214***

121.4

Income

1.656***

65.6

1.522***

52.2

1.588***

58.8

1.580***

58.0

1.459***

45.9

Mothereducation

Highscho

ol0.649*

−35.1

1.121***

12.1

0.774

−22.6

0.645**

−35.5

0.966

−3.4

Somecollege

0.868

−13.2

1.315***

31.5

1.085

8.5

1.321

32.1

1.524***

52.4

Bachelor’s

1.348*

34.8

1.619***

61.9

1.570**

57.0

1.506***

50.6

1.663***

66.3

Unkno

wn

0.194

−80.6

0.516***

−48.9

0.956

−4.4

0.392***

−60.8

N300,870

256,684

282,653

247,329

252,142

*p<0.05,**p<0.01,***

p<0.001.

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Table4.

Logitregression

outputs(odd

sratio

andpercentchange

inod

ds)–lowestcriteria.

L1L2

L3L4

L5

Year

2008

Year

2007

Year

2006

Year

2005

Year

2004

Variables

pnsci

%pnsci

%pnsci

%pfono

%pfono

%

Race Black

0.658***

−34.2

0.720***

−28.0

0.698***

−30.2

0.745***

−25.5

0.725***

−27.5

Brow

n0.518***

−48.2

0.648***

−35.2

0.505***

−49.5

0.662***

−33.8

0.492***

−50.8

Indigeno

us0.422***

−57.8

0.520***

−48.0

0.549***

−45.1

0.587***

−41.3

0.666***

−33.4

Asian

1.460***

46.0

0.876***

−12.4

1.419***

41.9

1.015

1.5

1.266***

26.6

Female

0.535***

−46.5

0.751***

−24.9

0.421***

−57.9

0.684***

−31.6

0.438***

−56.2

Age

0.967***

−3.3

0.928***

−7.2

0.832***

−16.8

0.863***

−13.7

0.844***

−15.6

Publicscho

ol0.258***

−74.2

0.197***

−80.3

0.198***

−80.2

0.215***

−78.5

0.218***

−78.2

Prep

course

2.348***

134.8

1.342***

34.2

2.052***

105.2

1.479***

47.9

2.003***

100.3

Income

1.526***

52.6

1.406***

40.6

1.460***

46.0

1.350***

35.0

1.377***

37.7

Mothereducation

HighScho

ol1.313***

31.3

1.430***

43.0

1.365***

36.5

1.487***

48.7

1.384***

38.4

Somecollege

1.684***

68.4

1.737***

73.7

1.661***

66.1

1.987***

98.7

1.722***

72.2

Bachelor’s

1.797***

79.7

1.716***

71.6

1.886***

88.6

2.061***

106.1

1.980***

98.0

Unkno

wn

0.725***

−27.5

0.711***

−28.9

0.618***

−38.2

0.621***

−37.9

0.641***

−35.9

N300,870

256,684

282,653

271,183

252,142

*p<0.05,**p<0.01,***

p<0.001.

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cent less for brown students than for white students, ceteris paribus. The term ‘ceterisparibus’ implies that when holding all other students’ characteristics such as socio-economic status constant, black and brown students have a significantly lower chanceof being admitted than a white student, meaning that these students are identical inrelation to all other variables, with the exception of race. The same relationship isobserved for each year analyzed. For the years of 2005 and 2006, no brown studentmet the threshold for acceptance in Medicine, thus these variables were omitted fromthe analysis. In 2006, we observe the same phenomenon among indigenous students.

Measures of socioeconomic status displayed by students’ income, mother education,and attendance of public school influenced the chances of admission as well. The oddsof being accepted are reduced by 85.6 per cent for students from public schools inmodel H1. Likewise, the higher the income, the higher the odds of acceptance, andhaving a mother with a bachelor’s degree significantly increases the odds of acceptance.These indicators are significant throughout all years analyzed.

When analyzing the model results in Table 4 for the lowest ENEM threshold criteria,all interactions are significant for black and brown students, and even though thethreshold was much lower, the results show that nonwhite students are less likely tobe admitted into the lowest criteria than white students for every year. Holding constantsocioeconomic status, gender, and age, black and brown students have significantlylower odds of being admitted than white students. Model L1 shows that the odds ofbeing accepted are 34.2 per cent less for black students than for white students and 48.2per cent less for brown students than for white students.

Likewise, socioeconomic status plays a significant role. For every single year, thehigher mother’s education, the higher the odds of being accepted. Similarly, the higherone’s income, the higher the odds of acceptance, whereas studying in a public highschool significantly decreases the odds of acceptance for every single year. One inter-esting finding is that the Asian racial category is negative for Natural Science in 2007 andnot significant in 2005. This is in sharp contrast to the highly significant effect in thehighest criteria major shown in Table 3. A possible reason could be due to the fact that agreater number of students were able to meet the threshold in 2007, in comparison toother racial groups, since the threshold was much lower in comparison to other years.Another possible explanation is that Asian students who are high performers concen-trate in the most prestigious majors such as Medicine and Engineering, while theweakest students of this same group tend to aim for less prestigious majors with lesscompetition. Future research is needed to better understand this finding.

Figures 1 and 2 show the modeled probability of being accepted for the highest andlowest criteria, respectively, against race for the students in the two models across the 5years period analyzed in this study, ceteris paribus (i.e., subject to controls). Theyillustrate the continuities in the admission process, which vary monotonically acrossrace and show a great and constant disparity between racial groups. The relationship isnot necessarily causal22 but there is other evidence that bears on causality. Whencomparing the racial distribution of the actual students admitted to USP, the resultsare comparable to the logistic models. The racial differentials found in this study areanalogous to the actual racial distribution of students admitted to USP between 2004and 2008, as indicated in Tables 5 and 6. Black and brown students are admitted at amuch lower per centage than white students. Likewise, the proportion of students

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admitted at USP by type of high school attended is comparable to the findings of thisstudy, as indicated in Tables 7 and 8.

Discussion

The effects of race on admission

The primary conclusion drawn from the analyses is that race and socioeconomic indi-cators pose a substantial barrier to admission at USP. Of particular significance is the rolethat race plays independently of socioeconomic status and other variables. The logistic

Figure 1. Predicted probability of acceptance in highest criteria by race.

Figure 2. Predicted probability of acceptance in lowest criteria by race.

Table 5. All majors acceptance by race at USP, 2008–2004.Race 2008 2007 2006 2005 2004

White 8683 (77.7%) 8743 (77.2%) 8866 (77.4%) 8565 (77.1%) 8064 (79.8%)Black 220 (2.0%) 217 (1.9%) 180 (1.6%) 175 (1.6%) 162 (1.6%)Brown 1219 (10.9%) 1193 (10.5%) 1154 (10.1%) 1162 (10.5%) 864 (8.5%)Indigenous 26 (0.2%) 42 (0.4%) 40 (0.3%) 37 (0.3%) 27 (0.3%)Asian 1024 (9.2%) 1137 (10%) 1209 (10.6%) 1176 (10.6%) 993 (9.8%)Total 11,172* 11,332* 11,449* 11,115* 10,110*

*The response rate for this question was roughly 99.0% for all years.Source: [Fuvest Questionário de Avaliação Sócio-Econômica- Candidatos Matrículados após a última chamada pela carreira emque se inscreveu, Total das Carreiras – Questão 16: Entre as alternativas abaixo, qual é a sua cor? 2004–2008].

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regression results display disturbing racial disparity in the probability of acceptance atUSP – black and brown students have a much lower chance of being accepted at USPthan white students. Race, regardless of the student’s class, is a statistically significantfactor in determining admission at USP. This is important as it provides support forarguments in favor of racial quotas.

Interestingly, the logistic regression analyses suggest that brown students have lowerodds of acceptance than black students for all models. Why should this be? A recentwork by Marteleto (2012) sheds some light on this trend. When analyzing the PesquisaNacional de Amostra por Domicílo (PNAD) from 1982 and 1987 to 2007, she found thatyounger cohorts of black adolescents had higher levels of schooling than their pardocounterparts. This educational advantage of blacks over pardos could be due to aphenomenon that Marteleto (2012) coins as ‘darkening with education.’ Recent cohortsof highly educated Brazilians may be labeling their children as black rather than either

Table 6. Medicine acceptance by race at USP, 2008–2004.Race 2008 2007 2006 2005 2004

White 276 (76.2%) 281 (78.3%) 289 (77.1%) 285 (75.8%) 292 (78.3%)Black 1 (0.3%) 5 (1.4%) 1 (0.3%) 2 (0.5%) 0 (0%)Brown 19 (5.2%) 23 (6.4%) 23 (6.1%) 27 (7.2%) 24 (6.4%)Indigenous 2 (0.6%) 1 (0.3%) 2 (0.5%) 1 (0.3%) 0 (0%)Asian 64 (17.7%) 49 (13.6%) 60 (16.0%) 61 (16.2%) 57 (15.3%)Total 362* 359* 375* 376* 373*

*This represents roughly 99% of students accepted into this majorSource: [Fuvest Questionário de Avaliação Sócio-Econômica – Candidatos Matrículados após a última chamada pelacarreira em que se inscreveu, Total das Carreiras – Questão 16: Entre as alternativas abaixo, qual é a sua cor?2004–2008].

Table 7. All majors acceptance by type of school at USP, 2008–2004.Type of school 2008 2007 2006 2005 2004

Private 7823 (69.7%) 8029 (70.6%) 8313 (72.2%) 7878 (70.6%) 7261 (71.6%)Public 2757 (24.6%) 2806 (24.7%) 2559 (22.2%) 2687 (24.1%) 2297 (22.6%)Abroad 64 (0.6%) 23 (0.2%) 16 (0.1%) 18 (0.2%) 21 (0.2%)Mostly private 340 (3.0%) 291 (2.6%) 304 (2.6%) 276 (2.5%) 301 (3.0%)Mostly public 194 (1.7%) 172 (1.5%) 216 (1.9%) 201 (1.8%) 179 (1.8%)Other 46 (0.4%) 56 (0.5%) 111 (1.0%) 100 (0.9%) 87 (0.9%)Total 11224* 11377* 11519* 11160* 10146*

*The response rate for this question was 99.0% for all years.Source: [Fuvest Questionário de Avaliação Sócio-Econômica- Candidatos Chamados para Primeira Matrícula pela carreiraem que se inscreveu, Total das Carreiras – Questão 7: Onde você realizou seus estudos de ensino médio? 2004–2008].

Table 8. Medicine acceptance by type of school at USP, 2008–2004.Type of school 2008 2007 2006 2005 2004

Private 325 (88.8%) 316 (86.8%) 340 (90.9%) 355 (93.9%) 346 (92.5%)Public 22 (6.0%) 42 (11.5%) 18 (4.8%) 17 (4.5%) 18 (4.8%)Abroad 1 (0.3%) 0 (0%) 1 (0.3%) 0 (0%) 0 (0%)Mostly private 15 (4.1%) 4 (1.1%) 11 (2.9%) 3 (0.8%) 7 (1.9%)Mostly public 3 (0.8%) 2 (0.5%) 4 (1.1%) 3 (0.8%) 2 (0.5%)Other 0 (0%) 0 (0%) 0 (0%) 0 (0%) 1 (0.3%)Total 366* 364* 374* 378* 374*

*This represents roughly 99.5% of students accepted into this major.Source: [Fuvest Questionário de Avaliação Sócio-Econômica- Candidatos Chamados para Primeira Matrícula pela carreiraem que se inscreveu, Carreira Medicina e Ciências Médicas – Questão 7: Onde você realizou seus estudos de ensinomédio? 2004–2008].

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white or pardo, a process that mirrors the concept of ‘whitening’ with money (Hasenbalg2005; Ianni 1987; Marteleto 2012; Schwartzman 2007). This elucidation is consistent withanalyses of the 2000 Brazilian census, which indicated a growing preference for thepolarized classification of black and white, suggesting a change in racial classificationwith increasing number of blacks (Telles 2004). Future research on racial labeling andracial perception is needed to further evaluate this phenomenon and test whetherdarkening with education is indeed the reason why black students seem to have anadvantage over pardos when seeking admission at USP.

The effects of socioeconomic status on admission

The results also indicate that poor students are less likely to be admitted to USP, andother socioeconomic factors, such as mother’s education, income, and type of schoolplay a significant role in acceptance as well. The more educated the student’s mother is,the more likely the student is to score higher in the ENEM exam and be admitted to USP.Income seems to have the same influence as mother’s education (as both variables arehighly correlated), since more resources are available for the student’s educationalpurposes. Most importantly, attending private school and prep courses significantlyincreases students’ likelihood of being admitted. This suggests that high-income stu-dents, due to their economic status, will experience a better quality of education, willscore higher on the ENEM exam and will greatly increase their odds of being admitted atUSP or at other public universities. The results therefore posit that there is a relationshipbetween socioeconomic status and vestibular acceptance. For all the years analyzed,socioeconomic indicators (type of school, income, prep course, and mother’s education)also had a strong impact on students’ chances of meeting the threshold for admissionto USP.

Among other sociodemographics that negatively influence acceptance at USP are theage and gender of the student. These differentials suggest that compared with allstudents seeking university education in Brazil, those accepted at USP are more likelyto be white, to come from high-income families, to come from private high schools, toenroll in a vestibular prep course, and to have a mother with high educational attain-ment. Additional studies with better data design are needed to measure these relation-ships directly and to confirm these findings.

Limitations

The results of this study should be considered in light of several methodologicallimitations. First, USP is the best university of Latin America,23 thus the characteristicsof students admitted at this university might differ greatly from students admitted atothers public universities in Brazil. Further research is needed to generalize thesefindings to other Brazilian universities. Second, due to data limitation, it was not possibleto use multilevel modeling to account for the variability associated with the clustering ofstudents within a given school. Thus, we did not control for school-level influencesbecause individual school data were not available. Third, the ENEM score analyzedcorresponded to the objective part of the exam and many studies have shown thatwomen tend to perform worse than men on objective exams. Although the ENEM exam

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has an essay part, this was not included in the analysis because the grading is subjectiveto the person grading the exam. Hence, a gender bias should be accounted for wheninterpreting the results, particularly because it is known that women actually comprise56 per cent of undergraduate students enrolled in Brazilian institutions of highereducation and this predominance at the university level seems to be a lasting trend(Tomelin 2002). Finally, a proxy for acceptance was used due to the inherent limitationof the data available; whether or not a student actually took the vestibular exam at USPand was admitted cannot be determined. As a result, even though a student might havetaken the ENEM exam, he/she may not have taken the USP vestibular and thereforeshould not have been considered in the analysis. Nevertheless, the results achieve asignificant degree of fit with patterns that emerged from the descriptive statistical dataprovided online by FUVEST, which shows the racial and economic characteristics ofstudents admitted in the same years analyzed in this study (see Tables 5–8).

Conclusion

Despite these limitations, this study has important implications for research and policy.The statistical evidence suggests that the relationship between class, race, and access tohigher education observed in this case study support Hasenbalg’s theory of cumulativeracial disadvantage over the life cycle – nonwhite students are at a major disadvantagewhen taking the vestibular at USP, not only because of their socioeconomic status, butalso because of their race. Thus, the inability to access higher education due to racedirectly influences the position in the labor market that will determine a person’s socialstatus in adulthood, and contributes to the persistent social, political, and incomeinequality in Brazil.

Several policy implications can be drawn from these findings. The public educationalsystem in Brazil is of poor quality and does not meet the rigorous requirements foradmission in public institutions of higher education. As the results suggest, attendingpublic school reduces the odds of being accepted in the most competitive degree ofMedicine at USP by an average of 88.66 per cent, and in the least competitive majors,Natural Sciences and Fonoaudiology, by 78.28 per cent. Therefore, policies to improvethe quality of public high schools are urgent, as it will ensure that poor families will havethe incentive, condition, and satisfactory welfare to send their children to public school,knowing that public college will be accessible in their future. In addition, the magnitudeof the demand for higher education vis-à-vis supply is not being met and creates anadditional problem. As in the case of USP, in November of 2012, about 159,000 peopleregistered for the vestibular, and only 10,982 students were selected at the USP campuswith an additional 100 students for the major of Medicine at the Medical School at SantaCasa São Paulo (USP 2012). Thus, it is imperative that the government also allocatesmore resources to public universities in order to help them grow and build moreinstitutions of higher education to meet the growing demand of students, especiallyfrom poor nonwhite candidates coming from public high schools.

Most importantly, the results posit that racial quotas for black, brown, and indigenousgroups are essential to reduce the racial disparity observed in this case study. In 2012,the Brazilian government took an important first step by signing into law Lei das Cotas,(or Quota Law) reserving 50 per cent of slots in federal universities to students from

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public high schools24 (Carvalhaes, Daflon, and Júnior 2013). Within this quota, half of theslots will be allocated to students whose family income is lower or equal to 1.5 minimumwage per person in the household, and within this quota slots will be reserved forblacks, browns, and indigenous people in proportion to the per centage of these groupswithin the state where the university is located (Carvalhaes, Daflon, and Júnior 2013).According to this new law, if specified racial groups do not fill the quota slots, then theavailable slots will go to students who studied only in public high school. The law wasput into effect in 2013, although federal universities have 4 years (until 2016) toimplement the necessary changes.

Lei das Cotas is only applicable to federal institutions of higher education. Stateuniversities, such as USP, can choose whether or not to adopt any form of affirmativeaction. Although USP and many other public state universities provide small bursaries25

for students coming from public schools in the vestibular, they are not required toimplement racial affirmative action. Recently, black movement activists have protestedat USP, invading classes to bring attention to the racial disparity of the student body. Arecent article in the Folha de São Paulo newspaper reported that USP has more Africanstudents than Afro-Brazilian students and that the only black professor in the entireuniversity, who was also from Africa, has already retired (Bergamim 2012). In a society asdiverse as Brazil, this is disconcerting news. If approved by USP, an affirmative actionpolicy based on race indicators will be a significant step toward equality and diversity.

This study calls for the development of more policies to promote access to highereducation for underrepresented groups and to the creation of new institutions of highereducation to meet a growing demand. In particular, affirmative action policies are effectivemechanisms for democratizing access to higher education and for expanding access ofnonwhite and poor youth to universities in Brazil. Given the controversial debate overaffirmative action programs in Brazil, the findings presented in this paper are significantand certainly lend credence to arguments in support of affirmative action, particularly ofracial quotas, albeit a combination of race- and social-based quotas may be the mosteffective tool for university accessibility. Future research testing the effectiveness of this lawin creating equity and diversity in institutions of higher education and whether it will resultin an overall diminishment of social and economic inequality in Brazil are vital in determin-ing its success. Providing access to higher education to all racial groups in Brazil is essentialtoward reducing and ensuring social, political, and economic equity.

Notes

1. The vestibular is a competitive exam that is the primary entrance system used by Brazilianuniversities to select their students. Admission is based on students’ performance in thisexam alone. Recently, the National Secondary Education Exam (ENEM) is being used as themain criteria for admission by most universities through the Unified Selection System(SISU). The University of São Paulo, however, continues to use solely the vestibular foradmission. As an experiment, in 2016, the university will make 13 per cent of its slotsavailable through SISU – whether this will continue remains to be seen.

2. In this paper, the term nonwhite is used to refer to those who are a mixture of white, black,and native Indian, classified as pardo in Brazil, but also to negro (black) or to mulato ormoreno (mixed of black and white).

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3. Race in Brazil is based on skin color and not racial identity, as a result of the high levels ofmiscegenation and multiracial ancestry of most Brazilians. Thus, Brazil has a very complexconcept of racial categories. This will be discussed later, but for an in-depth discussions seeHarris (1964); Hasenbalg (1979); Ianni (1960); Schwartzman (2007); Telles (2004, 2014);Telles and Lim (1998).

4. ENADE is one of the assessment procedures of the National Higher Education EvaluationSystem (Sistema Nacional de Avaliação da Educação Superior, or SINAES). ENADE is con-ducted by the Instituto Nacional de Estudos e Pesquisas Educacionais Anísio Teixeira (INEP)and was created in 2004 by the Ministry of Education. The exam assesses the developmentand acquisition of abilities and knowledge of students at the college level nationwide.

5. FUVEST is a federal agency that prepares and administers the vestibular of the University ofSão Paulo.

6. Brazil was the last country in the western hemisphere to abolish slavery in 1888.7. Low levels of educational attainment among nonwhites are also a result of structural

problems, lack of access to schools, and high fertility levels (Barros and Lam 1996;Birdsall and Sabot 1996; Lam and Levison 1991). For more, see also Marteleto (2012).

8. In 2010, with the implementation of the Sistema de Seleção Unificada (SISU), most publicinstitutions are now using the Exame National do Ensino Médio (ENEM) as mode ofadmission, which we discuss in the next subsection.

9. USP no longer uses ENEM to provide bonuses on the vestibular. Since 2010, the solecriterion for admission is the FUVEST exam (vestibular).

10. In 2009, 53 universities and institutes adhered to SISU. They are listed in Appendix. On 24June 2015, USP agreed to allocate 1489 slots to students to be selected through SISU forthe admission process of 2016, while the remaining 9568 slots will continue to be allocatedthrough the FUVEST vestibular. This will be an ‘experiment,’ and only applies to certainschools at USP and selected majors in Math and Sciences, Humanities, and BiologicalSciences (USP 2015).

11. In addition, some universities including the University of São Paulo use the student’s ENEMscore as supplement to their vestibular score depending on the student’s performance.

12. See FUVEST (2012).13. These are the only data available to test these relationships. Ideally, data for all students

who took the vestibular exam are needed to compare those who passed and those whodid not directly. However, after several attempts, FUVEST declined my requests to providesuch dataset and were only able to provide the average ENEM score and standarddeviation of each admitted class by major. Using the ENEM dataset, the datasets andmatched student’s characteristics were aligned to determine what students would havethe ENEM scores of those admitted at USP. As such, causality cannot be established.Nevertheless, as discussed later, the data yield very similar results to the actual socialand racial distribution of students admitted to USP.

14. The ENEM Socioeconomic Questionnaire can be found at http://portal.inep.gov.br/basica-levantamentos-acessar

15. For the years of 2004, 2005, 2006, 2007, and 2008, there were a total of 86, 98, 101, 105,and 107 majors available at USP, respectively.

16. The Natural Sciences major offers an integrated degree in Mathematics, Natural Sciences,and Education, and is designed to prepare science teachers for the elementary and primaryeducation levels. Graduates are also qualified to teach at the secondary education level inBiology, Physics, and Chemistry or to work in museums, centers of science, parks, NGOs,etc.

17. The Fonoaudiology major is equivalent to a speech therapy major.18. In 1976, IBGE conducted an interview to determine how to define those who were not

white, black, Asian, or indigenous (DataFolha 1995) and the result showed that Braziliansself-classified in 135 different colors. Several of the expressions used described physicalattributes beyond skin color such as crioula and agalegada (for the complete list, seeLevine and Crocitti (1999, 386–390). However, as pointed out by Petruccelli (2002) and

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Valle Silva (1994), despite the great number of terms, just seven categories correspondedto 95 per cent of the answers, and most of these are the same as the official terminology.Based on this survey, it was decided to use pardo for those who did not fit in the otherracial categories.

19. Pardo is the official category used by IBGE to identify those who are a mixture of white,black, and native Indian. The terms pardo and mulato are used interchangeably in thisstudy, as they represent someone of a mixed inheritance.

20. See for example, Hasenbalg (1988); Marteleto (2012); Telles (2004).21. During the period of this study, there was a strong presence of volunteers, mainly former

undergraduate students, who provided prevestibular courses to low-income students inpoor areas (Maggie 2001; Santos 2007). Nonetheless, being part of any prevestibular coursecan influence admission, and is therefore an important control variable.

22. Given that the dataset is a cross-sectional survey based on subjective assessments, selec-tion bias and unobserved variable bias can be potential limitations to the analysis.

23. See http://www.topuniversities.com/university-rankings/latin-american-university-rankings/2015

24. The selection of students within this quota system will be determined by their score on theENEM exam.

25. Through the Programa de Inclusão Social da USP (Inclusp) program, students whoattended both public middle and high schools receive 15 per cent bonus on theirvestibular score, students who attended public high school receive 12 per cent bonus,and nonwhite students receive an additional 5 per cent bonus. This program is started in2011.

Acknowledgements

The author would like to thank Brian J. L. Berry, Richard K. Scotch, Frederico S. Araujo, and Sarah R.Valente, two anonymous reviewers, and Peter Wade for their valuable comments and suggestions.The author also gratefully acknowledges Renan Leite, from FUVEST, who provided invaluableassistance and data that made this article possible.

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Barros, R., and D. Lam. 1996. “Income and Educational Inequality and Children’s SchoolingAttainment.” In Opportunity foregone: Education in Brazil, edited by N. Birdsall and R. Sabot,337–366. Washington, DC: Inter-American Development Bank.

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Appendix

List of the first institutions to start using the Sistema de Seleção Unificada (SISU) as admissioncriteria instead of the vestibular:

(1) Universidade Federal do Amazonas(2) Universidade Federal de Mato Grosso(3) Universidade Federal do Piauí(4) Universidade Federal de São João del Rei(5) Universidade Federal do Maranhão(6) Universidade Federal Rural do Rio de Janeiro(7) Universidade Federal Rural de Pernambuco(8) Universidade Tecnológica Federal do Paraná(9) Universidade Federal Rural do Semi-Árido(10) Universidade Federal de São Paulo(11) Universidade Federal de Lavras(12) Universidade Federal de Alfenas(13) Universidade Federal dos Vales do Jequitinhonha e Mucuri(14) Universidade Federal de Itajubá. Unifei(15) Universidade Federal do Recôncavo da Bahia(16) Universidade Federal de Pelotas(17) Universidade Federal do Estado do Rio de Janeiro(18) Fundação Universidade Federal de Rondonia(19) Fundação Universidade Federal de Ciências da Saúde de Porto Alegre(20) Fundação Universidade Federal do Tocantins(21) Fundação Universidade Federal do Vale do São Francisco(22) Fundação Universidade Federal do ABC(23) Fundação Universidade Federal do Pampa (Unipampa)(24) Instituto Federal de Educação, Ciência e Tecnologia da Bahia(25) Instituto Federal de Educação, Ciência e Tecnologia do Maranhão(26) Instituto Federal de Educação, Ciência e Tecnologia do Rio Grande do Sul(27) Instituto Federal de Educação, Ciência e Tecnologia Farroupilha(28) Instituto Federal de Educação, Ciência e Tecnologia do Rio Grande do Norte(29) Instituto Federal de Educação, Ciência e Tecnologia Fluminense(30) Instituto Federal de Educação, Ciência e Tecnologia da Paraíba(31) Instituto Federal de Educação, Ciência e Tecnologia Goiano(32) Instituto Federal de Educação, Ciência e Tecnologia do Espírito Santo

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(33) Instituto Federal de Educação, Ciência e Tecnologia de São Paulo(34) Instituto Federal de Educação, Ciência e Tecnologia de Goiás(35) Instituto Federal de Educação, Ciência e Tecnologia do Pará(36) Instituto Federal de Educação, Ciência e Tecnologia do Piauí(37) Instituto Federal de Educação, Ciência e Tecnologia de Alagoas(38) Instituto Federal de Educação, Ciência e Tecnologia de Santa Catarina(39) Instituto Federal de Educação, Ciência e Tecnologia do Rio de Janeiro(40) Instituto Federal de Educação, Ciência e Tecnologia do Triângulo Mineiro(41) Instituto Federal de Educação, Ciência e Tecnologia de Sergipe(42) Instituto Federal de Educação, Ciência e Tecnologia de Roraima(43) Instituto Federal de Educação, Ciência e Tecnologia do Norte de Minas Gerais(44) Instituto Federal de Educação, Ciência e Tecnologia do Sudeste de Minas Gerais(45) Instituto Federal de Educação, Ciência e Tecnologia de Brasilia(46) Instituto Federal de Educação, Ciência e Tecnologia Baiano(47) Instituto Federal de Educação, Ciência e Tecnologia do Tocantins(48) Instituto Federal de Educação, Ciência e Tecnologia Catarinense(49) Instituto Federal de educação, Ciencia e Tecnologia do Tocantins(50) Instituto Federal de educação, Ciencia e Tecnologia Catarinense(51) Cefet Celso Suckow da Fonseca (RJ)(52) Escola Nacional de Ciências Estatísticas (RJ)(53) Universidade Estadual do Norte Fluminense Darcy Ribeiro (RJ)

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